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AutomateLab-tech

Citation Intelligence MCP

signals_wikipedia

Read-onlyIdempotent

Find Wikipedia articles that reference a given domain to assess citation signals for LLM training data. Specify the domain and optional language.

Instructions

List Wikipedia articles that reference the given domain. Read-only. One HTTPS GET to the Wikipedia API (en.wikipedia.org/w/api.php?action=query&list=exturlusage). No auth required; no API keys; no rate limits beyond Wikipedia's public API fair-use policy (~1 request/second). Returns article titles and URLs. Wikipedia backlinks are the highest-lift signal for LLM training corpora — a domain cited from Wikipedia is far more likely to appear in AI training data and citation pools. Use lang to query non-English Wikipedias.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoWikipedia language subdomain, e.g. 'en', 'de', 'fr'.en
limitNoMaximum mention rows to return.
domainYesDomain to search for, e.g. 'automatelab.tech' (without protocol).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
langYesWikipedia language subdomain used.
totalYesNumber of Wikipedia articles referencing this domain.
domainYesDomain that was searched.
mentionsYesList of Wikipedia articles that cite the domain.
fetched_atYesUTC ISO-8601 timestamp.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.2

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes well beyond the annotations by disclosing the exact endpoint (en.wikipedia.org/w/api.php?action=query&list=exturlusage), the absence of auth/API keys, the external rate limit (~1 request/second fair-use), and the return shape (article titles and URLs). With annotations already covering the safety profile, this adds genuinely useful operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tight and front-loaded: the action comes first, then the implementation detail, then the rationale. The LLM-training-corpus sentence is longer than strictly necessary but carries decision-relevant motivation, so it largely earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value explanation is not required, yet the description still summarizes it. Combined with the endpoint, rate limit, auth status, and lang guidance, nothing an agent needs to invoke this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds a usage hint for lang ('Use lang to query non-English Wikipedias') that is not present in the schema's own field description. It does not characterize limit, leaving that to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a precise verb and resource ('List Wikipedia articles that reference the given domain'), which cleanly separates it from the broader signals_* and citations_* siblings. An agent can identify the tool's job without reading the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context for why and when to reach for it ('highest-lift signal for LLM training corpora') and how to extend it with lang. However, it never names a sibling it competes with or states an exclusion, so it stops short of explicit alternative routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.